Two-stage stochastic programming-based capacity optimization for a high-temperature electrolysis system considering dynamic operation strategies

2021 ◽  
Vol 40 ◽  
pp. 102733
Author(s):  
Ruomei Qi ◽  
Yiwei Qiu ◽  
Jin Lin ◽  
Yonghua Song ◽  
Wenying Li ◽  
...  
Keyword(s):  
High Temperature ◽  
Stochastic Programming ◽  
Capacity Optimization ◽  
Two Stage ◽  
Dynamic Operation ◽  
Electrolysis System ◽  
High Temperature Electrolysis

Assessment of thermodynamic performance of a 20 kW high‐temperature electrolysis system using advanced exergy analysis

Fuel Cells ◽  
2021 ◽  
Author(s):  
Guoliang Li ◽  
Guoping Xiao ◽  
Chengzhi Guan ◽  
Chunfeng Hong ◽  
Benfeng Yuan ◽  
...  
Keyword(s):  
High Temperature ◽  
Exergy Analysis ◽  
Thermodynamic Performance ◽  
Electrolysis System ◽  
High Temperature Electrolysis

Efficiency of a conceptual design of a high temperature electrolysis system coupled to a VHTR for nuclear hydrogen production

2016 ◽  
Vol 3 (1) ◽  
pp. 32
Author(s):  
Daniel González Rodríguez ◽  
Carlos Alberto Brayner de Oliveira Lira ◽  
Lázaro García Parra ◽  
Carlos García Hernández ◽  
Raciel De la Torre Valdés
Keyword(s):  
High Temperature ◽  
Hydrogen Production ◽  
Conceptual Design ◽  
Electrolysis System ◽  
High Temperature Electrolysis

scholarly journals HYFIRE: A TOKAMAK–HIGH-TEMPERATURE ELECTROLYSIS SYSTEM

1981 ◽  
pp. 1215-1221
Author(s):  
J.A. Fillo ◽  
J.R. Powell ◽  
M. Steinberg ◽  
R. Benenati ◽  
F. Horn ◽  
...  
Keyword(s):  
High Temperature ◽  
Electrolysis System ◽  
High Temperature Electrolysis

scholarly journals A column-and-constraint generation algorithm for two-stage stochastic programming problems

Top ◽  
2021 ◽  
Author(s):  
Denise D. Tönissen ◽  
Joachim J. Arts ◽  
Zuo-Jun Max Shen
Keyword(s):  
Stochastic Programming ◽  
Benders Decomposition ◽  
Equivalent Model ◽  
Computational Time ◽  
Two Stage ◽  
Generation Algorithm ◽  
Constraint Generation ◽  
Deterministic Equivalent ◽  
Location Routing ◽  
Relative Tolerance

AbstractThis paper presents a column-and-constraint generation algorithm for two-stage stochastic programming problems. A distinctive feature of the algorithm is that it does not assume fixed recourse and as a consequence the values and dimensions of the recourse matrix can be uncertain. The proposed algorithm contains multi-cut (partial) Benders decomposition and the deterministic equivalent model as special cases and can be used to trade-off computational speed and memory requirements. The algorithm outperforms multi-cut (partial) Benders decomposition in computational time and the deterministic equivalent model in memory requirements for a maintenance location routing problem. In addition, for instances with a large number of scenarios, the algorithm outperforms the deterministic equivalent model in both computational time and memory requirements. Furthermore, we present an adaptive relative tolerance for instances for which the solution time of the master problem is the bottleneck and the slave problems can be solved relatively efficiently. The adaptive relative tolerance is large in early iterations and converges to zero for the final iteration(s) of the algorithm. The combination of this relative adaptive tolerance with the proposed algorithm decreases the computational time of our instances even further.

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